
'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


_UpperCAmelCase :   str			    =	logging.get_logger(__name__)







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase						)			->   str:
		_UpperCamelCase							:			Union[str, Any]     =   b.T
		_UpperCamelCase							:			Union[str, Any]     =   np.sum(np.square(_A						)      ,axis=1						)
		_UpperCamelCase							:			Union[str, Any]     =   np.sum(np.square(_A						)      ,axis=0						)
		_UpperCamelCase							:			List[str]     =   np.matmul(_A      ,_A						)
		_UpperCamelCase							:			Dict     =   aa[:, None] - 2 * ab + ba[None, :]
		return d







def     snake_case__						(   UpperCamelCase      ,UpperCamelCase						)			->   Optional[int]:
		_UpperCamelCase							:			str     =   x.reshape(-1      ,3						)
		_UpperCamelCase							:			Union[str, Any]     =   squared_euclidean_distance(_A      ,_A						)
		return np.argmin(_A      ,axis=1						)


class       UpperCAmelCase					(       lowerCamelCase__			):





		"""simple docstring"""





		A__	: Union[str, Any]    =     ['pixel_values']
		def __init__(     self	,       _snake_case = None	,       _snake_case = True	,       _snake_case = None	,       _snake_case = PILImageResampling.BILINEAR	,       _snake_case = True	,       _snake_case = True	,       **_snake_case	,       )					->     Union[str, Any]:
				super().__init__(**__snake_case  )
				_UpperCamelCase							:			Tuple     =   size if size is not None else {'height': 256, 'width': 256}
				_UpperCamelCase							:			Union[str, Any]     =   get_size_dict(__snake_case  )
				_UpperCamelCase							:			Union[str, Any]     =   np.array(__snake_case  ) if clusters is not None else None
				_UpperCamelCase							:			Dict     =   do_resize
				_UpperCamelCase							:			List[str]     =   size
				_UpperCamelCase							:			Any     =   resample
				_UpperCamelCase							:			Union[str, Any]     =   do_normalize
				_UpperCamelCase							:			List[Any]     =   do_color_quantize
		def _lowercase	(     self	,       _snake_case	,       _snake_case	,       _snake_case = PILImageResampling.BILINEAR	,       _snake_case = None	,       **_snake_case	,       )					->     Optional[int]:
				_UpperCamelCase							:			List[str]     =   get_size_dict(__snake_case  )
				if "height" not in size or "width" not in size:
						raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}'''  )
				return resize(
				    __snake_case	,       size=(size['''height'''], size['''width'''])	,       resample=__snake_case	,       data_format=__snake_case	,       **__snake_case  )
		def _lowercase	(     self	,       _snake_case	,       _snake_case = None	,       )					->     Union[str, Any]:
				_UpperCamelCase							:			int     =   rescale(image=__snake_case	,       scale=1 / 127.5	,       data_format=__snake_case  )
				_UpperCamelCase							:			List[Any]     =   image - 1
				return image




		def _lowercase	(     self	,       _snake_case	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = None	,       _snake_case = ChannelDimension.FIRST	,       **_snake_case	,       )					->     Union[str, Any]:
				_UpperCamelCase							:			Optional[Any]     =   do_resize if do_resize is not None else self.do_resize
				_UpperCamelCase							:			Any     =   size if size is not None else self.size
				_UpperCamelCase							:			int     =   get_size_dict(__snake_case  )
				_UpperCamelCase							:			Optional[int]     =   resample if resample is not None else self.resample
				_UpperCamelCase							:			int     =   do_normalize if do_normalize is not None else self.do_normalize
				_UpperCamelCase							:			Optional[int]     =   do_color_quantize if do_color_quantize is not None else self.do_color_quantize
				_UpperCamelCase							:			Tuple     =   clusters if clusters is not None else self.clusters
				_UpperCamelCase							:			List[Any]     =   np.array(__snake_case  )

				_UpperCamelCase							:			Optional[int]     =   make_list_of_images(__snake_case  )

				if not valid_images(__snake_case  ):
						raise ValueError(
						    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
						    '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

				if do_resize and size is None or resample is None:
						raise ValueError('''Size and resample must be specified if do_resize is True.'''  )

				if do_color_quantize and clusters is None:
						raise ValueError('''Clusters must be specified if do_color_quantize is True.'''  )

				# All transformations expect numpy arrays.
				_UpperCamelCase							:			Optional[int]     =   [to_numpy_array(__snake_case  ) for image in images]

				if do_resize:
						_UpperCamelCase							:			Optional[Any]     =   [self.resize(image=__snake_case	,       size=__snake_case	,       resample=__snake_case  ) for image in images]

				if do_normalize:
						_UpperCamelCase							:			Union[str, Any]     =   [self.normalize(image=__snake_case  ) for image in images]

				if do_color_quantize:
						_UpperCamelCase							:			str     =   [to_channel_dimension_format(__snake_case	,       ChannelDimension.LAST  ) for image in images]
						# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
						_UpperCamelCase							:			List[str]     =   np.array(__snake_case  )
						_UpperCamelCase							:			Optional[Any]     =   color_quantize(__snake_case	,       __snake_case  ).reshape(images.shape[:-1]  )

						# flatten to (batch_size, height*width)
						_UpperCamelCase							:			str     =   images.shape[0]
						_UpperCamelCase							:			int     =   images.reshape(__snake_case	,       -1  )

						# We need to convert back to a list of images to keep consistent behaviour across processors.
						_UpperCamelCase							:			int     =   list(__snake_case  )
				else:
						_UpperCamelCase							:			Dict     =   [to_channel_dimension_format(__snake_case	,       __snake_case  ) for image in images]

				_UpperCamelCase							:			Optional[Any]     =   {'input_ids': images}
				return BatchFeature(data=__snake_case	,       tensor_type=__snake_case  )










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase:   List[Any]					=		{'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Dict					=		['ReformerTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Tuple					=		['ReformerTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ReformerAttention',
         'ReformerForMaskedLM',
         'ReformerForQuestionAnswering',
         'ReformerForSequenceClassification',
         'ReformerLayer',
         'ReformerModel',
         'ReformerModelWithLMHead',
         'ReformerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer import ReformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer_fast import ReformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_reformer import (
              REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              ReformerAttention,
              ReformerForMaskedLM,
              ReformerForQuestionAnswering,
              ReformerForSequenceClassification,
              ReformerLayer,
              ReformerModel,
              ReformerModelWithLMHead,
              ReformerPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase:   str					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)